Overview

Dataset statistics

Number of variables16
Number of observations943
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.7 KiB
Average record size in memory121.3 B

Variable types

DateTime1
Categorical8
Numeric7

Warnings

username has constant value "levie" Constant
tweet has a high cardinality: 943 distinct values High cardinality
video is highly correlated with photosHigh correlation
photos is highly correlated with videoHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
video is highly correlated with photosHigh correlation
photos is highly correlated with videoHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
video is highly correlated with photosHigh correlation
photos is highly correlated with videoHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
photos is highly correlated with urls and 1 other fieldsHigh correlation
bins is highly correlated with percent changeHigh correlation
urls is highly correlated with photosHigh correlation
replies_count is highly correlated with likes_count and 1 other fieldsHigh correlation
video is highly correlated with photosHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
percent change is highly correlated with binsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
bins is highly correlated with usernameHigh correlation
urls is highly correlated with usernameHigh correlation
cashtags is highly correlated with usernameHigh correlation
hashtags is highly correlated with usernameHigh correlation
username is highly correlated with bins and 5 other fieldsHigh correlation
video is highly correlated with usernameHigh correlation
mentions is highly correlated with usernameHigh correlation
tweet is uniformly distributed Uniform
date has unique values Unique
tweet has unique values Unique
photos has 820 (87.0%) zeros Zeros
replies_count has 28 (3.0%) zeros Zeros
retweets_count has 52 (5.5%) zeros Zeros
percent change has 19 (2.0%) zeros Zeros

Reproduction

Analysis started2021-09-27 19:02:55.560063
Analysis finished2021-09-27 19:03:05.860271
Duration10.3 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

date
Date

UNIQUE

Distinct943
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
Minimum2016-08-27 09:30:00
Maximum2021-07-20 16:00:00
2021-09-27T15:03:05.978871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:06.133098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct943
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
Everything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.
 
1
@saurabhbhatia Completely agree on both of these! Hope you begin to see these soon-ish...
 
1
If I owned this desk I would be asleep 93% of the working day. https://t.co/eAXPPuOTiG
 
1
I have yet to run into a business strategy problem that can’t be solved by a pyramid shape or a flywheel. After seeing this TikTok deal play out, I will never again say “ok that’s not realistic” after an episode of Billions.
 
1
This is extremely impressive. A well-timed digital strategy that now let’s them own their consumer relationship end-to-end.
 
1
Other values (938)
938 

Length

Max length2175
Median length144
Mean length208.6680806
Min length3

Characters and Unicode

Total characters196774
Distinct characters175
Distinct categories16 ?
Distinct scripts3 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique943 ?
Unique (%)100.0%

Sample

1st rowEverything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.
2nd rowThe Pope's face is one of those, "Shit, it's just a statue of a drone, I thought I was getting a real one" faces. https://t.co/AIURrVdXTt
3rd row6,000 people registered for BoxWorks next week! We'll be making major product announcements. And some Trump jokes. https://t.co/EXn9NZAbk0
4th rowUnlike essentially every other kind of challenge a startup runs into, you probably don't hear "it's not rocket science" much at SpaceX.
5th rowHopefully it's clear by now that promising taco trucks on every corner would, in fact, be the most electable platform to run on.

Common Values

ValueCountFrequency (%)
Everything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.1
 
0.1%
@saurabhbhatia Completely agree on both of these! Hope you begin to see these soon-ish...1
 
0.1%
If I owned this desk I would be asleep 93% of the working day. https://t.co/eAXPPuOTiG1
 
0.1%
I have yet to run into a business strategy problem that can’t be solved by a pyramid shape or a flywheel. After seeing this TikTok deal play out, I will never again say “ok that’s not realistic” after an episode of Billions.1
 
0.1%
This is extremely impressive. A well-timed digital strategy that now let’s them own their consumer relationship end-to-end.1
 
0.1%
You have to hand it to Facebook. They sure do know how to compete when it matters most. https://t.co/F0d58HRIKV1
 
0.1%
@mcannonbrookes Nah man, fits your portfolio much better... https://t.co/IA4Nl0le9Y Are you really even a tech company if you haven’t explored acquiring TikTok?1
 
0.1%
Plenty of startup opportunities appear to be in “crowded markets” when in reality the market is just customers solving problems in different ways all looking for a better solution.1
 
0.1%
We all miss you and are sorry about how the internet played out.1
 
0.1%
My back of the envelope math says that people are more excited by Kamala Harris than they were Tim Kaine. Boom1
 
0.1%
Other values (933)933
98.9%

Length

2021-09-27T15:03:06.510353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1216
 
3.7%
to1035
 
3.2%
a645
 
2.0%
and588
 
1.8%
of552
 
1.7%
is539
 
1.7%
in441
 
1.4%
this423
 
1.3%
for385
 
1.2%
that358
 
1.1%
Other values (5655)26306
81.0%

Most occurring characters

ValueCountFrequency (%)
31923
16.2%
e17494
 
8.9%
t14185
 
7.2%
o12715
 
6.5%
a11171
 
5.7%
n10215
 
5.2%
i10110
 
5.1%
s9472
 
4.8%
r9112
 
4.6%
l6434
 
3.3%
Other values (165)63943
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter147762
75.1%
Space Separator31926
 
16.2%
Other Punctuation7042
 
3.6%
Uppercase Letter6730
 
3.4%
Decimal Number1680
 
0.9%
Final Punctuation728
 
0.4%
Other Symbol262
 
0.1%
Dash Punctuation253
 
0.1%
Initial Punctuation104
 
0.1%
Connector Punctuation67
 
< 0.1%
Other values (6)220
 
0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
😂33
 
12.6%
😀28
 
10.7%
🙏24
 
9.2%
👏17
 
6.5%
12
 
4.6%
👍7
 
2.7%
😉5
 
1.9%
5
 
1.9%
😄5
 
1.9%
5
 
1.9%
Other values (69)121
46.2%
Lowercase Letter
ValueCountFrequency (%)
e17494
11.8%
t14185
 
9.6%
o12715
 
8.6%
a11171
 
7.6%
n10215
 
6.9%
i10110
 
6.8%
s9472
 
6.4%
r9112
 
6.2%
l6434
 
4.4%
h6350
 
4.3%
Other values (17)40504
27.4%
Uppercase Letter
ValueCountFrequency (%)
T744
 
11.1%
I699
 
10.4%
A472
 
7.0%
S460
 
6.8%
B458
 
6.8%
W443
 
6.6%
C313
 
4.7%
M251
 
3.7%
P234
 
3.5%
N231
 
3.4%
Other values (16)2425
36.0%
Other Punctuation
ValueCountFrequency (%)
.2478
35.2%
,1136
16.1%
/942
 
13.4%
@865
 
12.3%
:514
 
7.3%
!316
 
4.5%
'287
 
4.1%
"152
 
2.2%
*115
 
1.6%
?103
 
1.5%
Other values (4)134
 
1.9%
Decimal Number
ValueCountFrequency (%)
0450
26.8%
1277
16.5%
2241
14.3%
5128
 
7.6%
9108
 
6.4%
3105
 
6.2%
698
 
5.8%
495
 
5.7%
895
 
5.7%
783
 
4.9%
Math Symbol
ValueCountFrequency (%)
+17
53.1%
~8
25.0%
=5
 
15.6%
1
 
3.1%
1
 
3.1%
Space Separator
ValueCountFrequency (%)
31923
> 99.9%
 3
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-227
89.7%
26
 
10.3%
Final Punctuation
ValueCountFrequency (%)
628
86.3%
100
 
13.7%
Initial Punctuation
ValueCountFrequency (%)
102
98.1%
2
 
1.9%
Open Punctuation
ValueCountFrequency (%)
(41
100.0%
Close Punctuation
ValueCountFrequency (%)
)49
100.0%
Currency Symbol
ValueCountFrequency (%)
$66
100.0%
Connector Punctuation
ValueCountFrequency (%)
_67
100.0%
Nonspacing Mark
ValueCountFrequency (%)
27
100.0%
Format
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin154492
78.5%
Common42250
 
21.5%
Inherited32
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
31923
75.6%
.2478
 
5.9%
,1136
 
2.7%
/942
 
2.2%
@865
 
2.0%
628
 
1.5%
:514
 
1.2%
0450
 
1.1%
!316
 
0.7%
'287
 
0.7%
Other values (110)2711
 
6.4%
Latin
ValueCountFrequency (%)
e17494
 
11.3%
t14185
 
9.2%
o12715
 
8.2%
a11171
 
7.2%
n10215
 
6.6%
i10110
 
6.5%
s9472
 
6.1%
r9112
 
5.9%
l6434
 
4.2%
h6350
 
4.1%
Other values (43)47234
30.6%
Inherited
ValueCountFrequency (%)
27
84.4%
5
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII195615
99.4%
Punctuation863
 
0.4%
Emoticons141
 
0.1%
None87
 
< 0.1%
VS27
 
< 0.1%
Misc Symbols17
 
< 0.1%
Dingbats9
 
< 0.1%
Enclosed Alphanum Sup6
 
< 0.1%
Latin 1 Sup5
 
< 0.1%
Letterlike Symbols2
 
< 0.1%
Other values (2)2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31923
16.3%
e17494
 
8.9%
t14185
 
7.3%
o12715
 
6.5%
a11171
 
5.7%
n10215
 
5.2%
i10110
 
5.2%
s9472
 
4.8%
r9112
 
4.7%
l6434
 
3.3%
Other values (75)62784
32.1%
Emoticons
ValueCountFrequency (%)
😂33
23.4%
😀28
19.9%
🙏24
17.0%
😉5
 
3.5%
😄5
 
3.5%
😳4
 
2.8%
😬4
 
2.8%
😎4
 
2.8%
😢3
 
2.1%
😟3
 
2.1%
Other values (20)28
19.9%
Latin 1 Sup
ValueCountFrequency (%)
 3
60.0%
é2
40.0%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇺3
50.0%
🇸2
33.3%
🇪1
 
16.7%
None
ValueCountFrequency (%)
👏17
19.5%
👍7
 
8.0%
5
 
5.7%
🚀5
 
5.7%
🔥4
 
4.6%
🤪3
 
3.4%
🎉3
 
3.4%
👇3
 
3.4%
🤯3
 
3.4%
🧐3
 
3.4%
Other values (28)34
39.1%
Math Operators
ValueCountFrequency (%)
1
100.0%
Misc Symbols
ValueCountFrequency (%)
12
70.6%
2
 
11.8%
2
 
11.8%
1
 
5.9%
VS
ValueCountFrequency (%)
27
100.0%
Dingbats
ValueCountFrequency (%)
5
55.6%
3
33.3%
1
 
11.1%
Punctuation
ValueCountFrequency (%)
628
72.8%
102
 
11.8%
100
 
11.6%
26
 
3.0%
5
 
0.6%
2
 
0.2%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
Sup Arrows B
ValueCountFrequency (%)
1
100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
levie
943 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4715
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlevie
2nd rowlevie
3rd rowlevie
4th rowlevie
5th rowlevie

Common Values

ValueCountFrequency (%)
levie943
100.0%

Length

2021-09-27T15:03:06.761347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:03:06.831148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
levie943
100.0%

Most occurring characters

ValueCountFrequency (%)
e1886
40.0%
l943
20.0%
v943
20.0%
i943
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4715
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1886
40.0%
l943
20.0%
v943
20.0%
i943
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4715
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1886
40.0%
l943
20.0%
v943
20.0%
i943
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1886
40.0%
l943
20.0%
v943
20.0%
i943
20.0%

mentions
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
0
866 
1
 
58
2
 
17
4
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters943
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0866
91.8%
158
 
6.2%
217
 
1.8%
41
 
0.1%
31
 
0.1%

Length

2021-09-27T15:03:07.015901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:03:07.091242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0866
91.8%
158
 
6.2%
217
 
1.8%
41
 
0.1%
31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0866
91.8%
158
 
6.2%
217
 
1.8%
41
 
0.1%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number943
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0866
91.8%
158
 
6.2%
217
 
1.8%
41
 
0.1%
31
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common943
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0866
91.8%
158
 
6.2%
217
 
1.8%
41
 
0.1%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0866
91.8%
158
 
6.2%
217
 
1.8%
41
 
0.1%
31
 
0.1%

hashtags
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
0
932 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0932
98.8%
111
 
1.2%

Length

2021-09-27T15:03:07.296032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:03:07.366342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0932
98.8%
111
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0932
98.8%
111
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number943
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0932
98.8%
111
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common943
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0932
98.8%
111
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0932
98.8%
111
 
1.2%

cashtags
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
0
941 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0941
99.8%
12
 
0.2%

Length

2021-09-27T15:03:07.542234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:03:07.611995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0941
99.8%
12
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0941
99.8%
12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number943
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0941
99.8%
12
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common943
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0941
99.8%
12
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0941
99.8%
12
 
0.2%

video
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
0
808 
1
128 
2
 
5
4
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters943
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0808
85.7%
1128
 
13.6%
25
 
0.5%
41
 
0.1%
31
 
0.1%

Length

2021-09-27T15:03:07.822985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:03:07.898864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0808
85.7%
1128
 
13.6%
25
 
0.5%
41
 
0.1%
31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0808
85.7%
1128
 
13.6%
25
 
0.5%
41
 
0.1%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number943
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0808
85.7%
1128
 
13.6%
25
 
0.5%
41
 
0.1%
31
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common943
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0808
85.7%
1128
 
13.6%
25
 
0.5%
41
 
0.1%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0808
85.7%
1128
 
13.6%
25
 
0.5%
41
 
0.1%
31
 
0.1%

photos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1527041357
Minimum0
Maximum7
Zeros820
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-09-27T15:03:08.358051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4630859798
Coefficient of variation (CV)3.032569993
Kurtosis58.70723629
Mean0.1527041357
Median Absolute Deviation (MAD)0
Skewness5.657196384
Sum144
Variance0.2144486247
MonotonicityNot monotonic
2021-09-27T15:03:08.480520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0820
87.0%
1110
 
11.7%
210
 
1.1%
71
 
0.1%
31
 
0.1%
41
 
0.1%
ValueCountFrequency (%)
0820
87.0%
1110
 
11.7%
210
 
1.1%
31
 
0.1%
41
 
0.1%
71
 
0.1%
ValueCountFrequency (%)
71
 
0.1%
41
 
0.1%
31
 
0.1%
210
 
1.1%
1110
 
11.7%
0820
87.0%

urls
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
0
785 
1
152 
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters943
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0785
83.2%
1152
 
16.1%
26
 
0.6%

Length

2021-09-27T15:03:08.735699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:03:08.811752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0785
83.2%
1152
 
16.1%
26
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0785
83.2%
1152
 
16.1%
26
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number943
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0785
83.2%
1152
 
16.1%
26
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common943
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0785
83.2%
1152
 
16.1%
26
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0785
83.2%
1152
 
16.1%
26
 
0.6%

replies_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct165
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.69141039
Minimum0
Maximum1373
Zeros28
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-09-27T15:03:08.912602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median23
Q347.5
95-th percentile143
Maximum1373
Range1373
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation71.43682494
Coefficient of variation (CV)1.673330168
Kurtosis137.4067264
Mean42.69141039
Median Absolute Deviation (MAD)16
Skewness8.903154451
Sum40258
Variance5103.219958
MonotonicityNot monotonic
2021-09-27T15:03:09.062747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131
 
3.3%
1331
 
3.3%
028
 
3.0%
524
 
2.5%
1223
 
2.4%
323
 
2.4%
622
 
2.3%
1422
 
2.3%
922
 
2.3%
820
 
2.1%
Other values (155)697
73.9%
ValueCountFrequency (%)
028
3.0%
131
3.3%
218
1.9%
323
2.4%
417
1.8%
524
2.5%
622
2.3%
714
1.5%
820
2.1%
922
2.3%
ValueCountFrequency (%)
13731
0.1%
6581
0.1%
5141
0.1%
4491
0.1%
4021
0.1%
3791
0.1%
3151
0.1%
2981
0.1%
2961
0.1%
2571
0.1%

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct398
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.4220573
Minimum0
Maximum8667
Zeros52
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-09-27T15:03:09.224418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132
median95
Q3227
95-th percentile803.9
Maximum8667
Range8667
Interquartile range (IQR)195

Descriptive statistics

Standard deviation442.1322995
Coefficient of variation (CV)2.042916998
Kurtosis149.1851339
Mean216.4220573
Median Absolute Deviation (MAD)79
Skewness9.369359817
Sum204086
Variance195480.9703
MonotonicityNot monotonic
2021-09-27T15:03:09.376362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
052
 
5.5%
121
 
2.2%
2711
 
1.2%
4610
 
1.1%
210
 
1.1%
149
 
1.0%
139
 
1.0%
129
 
1.0%
238
 
0.8%
178
 
0.8%
Other values (388)796
84.4%
ValueCountFrequency (%)
052
5.5%
121
2.2%
210
 
1.1%
37
 
0.7%
46
 
0.6%
53
 
0.3%
66
 
0.6%
72
 
0.2%
86
 
0.6%
96
 
0.6%
ValueCountFrequency (%)
86671
0.1%
33921
0.1%
32051
0.1%
24791
0.1%
24011
0.1%
21101
0.1%
20161
0.1%
20131
0.1%
19511
0.1%
19311
0.1%

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct780
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1565.697773
Minimum0
Maximum47412
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-09-27T15:03:09.525041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.1
Q1370.5
median826
Q31786
95-th percentile5187.5
Maximum47412
Range47412
Interquartile range (IQR)1415.5

Descriptive statistics

Standard deviation2667.338067
Coefficient of variation (CV)1.703609798
Kurtosis109.318219
Mean1565.697773
Median Absolute Deviation (MAD)559
Skewness8.091379877
Sum1476453
Variance7114692.362
MonotonicityNot monotonic
2021-09-27T15:03:09.680312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56
 
0.6%
554
 
0.4%
44
 
0.4%
10924
 
0.4%
7524
 
0.4%
154
 
0.4%
74
 
0.4%
1334
 
0.4%
83
 
0.3%
5573
 
0.3%
Other values (770)903
95.8%
ValueCountFrequency (%)
01
 
0.1%
11
 
0.1%
22
 
0.2%
31
 
0.1%
44
0.4%
56
0.6%
61
 
0.1%
74
0.4%
83
0.3%
93
0.3%
ValueCountFrequency (%)
474121
0.1%
284191
0.1%
219181
0.1%
174691
0.1%
155751
0.1%
136561
0.1%
110681
0.1%
108891
0.1%
105931
0.1%
104791
0.1%

number of tweets
Real number (ℝ≥0)

Distinct15
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.80699894
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-09-27T15:03:09.806064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum15
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.601775092
Coefficient of variation (CV)0.8864283522
Kurtosis18.18554396
Mean1.80699894
Median Absolute Deviation (MAD)0
Skewness3.628235866
Sum1704
Variance2.565683447
MonotonicityNot monotonic
2021-09-27T15:03:09.914449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1594
63.0%
2183
 
19.4%
374
 
7.8%
440
 
4.2%
521
 
2.2%
610
 
1.1%
77
 
0.7%
84
 
0.4%
103
 
0.3%
122
 
0.2%
Other values (5)5
 
0.5%
ValueCountFrequency (%)
1594
63.0%
2183
 
19.4%
374
 
7.8%
440
 
4.2%
521
 
2.2%
610
 
1.1%
77
 
0.7%
84
 
0.4%
91
 
0.1%
103
 
0.3%
ValueCountFrequency (%)
151
 
0.1%
141
 
0.1%
131
 
0.1%
122
 
0.2%
111
 
0.1%
103
 
0.3%
91
 
0.1%
84
 
0.4%
77
0.7%
610
1.1%

price
Real number (ℝ≥0)

Distinct732
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.67166823
Minimum9.06000042
Maximum29.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-09-27T15:03:10.058611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9.06000042
5-th percentile14.17400004
Q116.91333357
median18.13499928
Q320.32208308
95-th percentile24.79199924
Maximum29.25
Range20.18999958
Interquartile range (IQR)3.408749501

Descriptive statistics

Standard deviation3.122952749
Coefficient of variation (CV)0.1672562253
Kurtosis0.575870231
Mean18.67166823
Median Absolute Deviation (MAD)1.534998894
Skewness0.4414069218
Sum17607.38314
Variance9.752833875
MonotonicityNot monotonic
2021-09-27T15:03:10.225237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.889999396
 
0.6%
18.799999246
 
0.6%
18.360000615
 
0.5%
18.559999475
 
0.5%
18.049999245
 
0.5%
16.920000084
 
0.4%
17.149999624
 
0.4%
17.379999164
 
0.4%
17.549999244
 
0.4%
17.799999244
 
0.4%
Other values (722)896
95.0%
ValueCountFrequency (%)
9.060000421
0.1%
9.1199998861
0.1%
9.2100000381
0.1%
9.2299995421
0.1%
9.5900001531
0.1%
9.7566668191
0.1%
10.453333221
0.1%
10.600000381
0.1%
10.930000311
0.1%
11.51
0.1%
ValueCountFrequency (%)
29.251
0.1%
28.420000081
0.1%
281
0.1%
27.350000381
0.1%
27.319999691
0.1%
27.212500571
0.1%
27.209999721
0.1%
27.200000761
0.1%
26.825000291
0.1%
26.77500011
0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct921
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001000680484
Minimum-0.1438071139
Maximum0.2148070316
Zeros19
Zeros (%)2.0%
Negative438
Negative (%)46.4%
Memory size7.5 KiB
2021-09-27T15:03:10.393432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1438071139
5-th percentile-0.0295389959
Q1-0.00768489594
median0.000870460276
Q30.009556436548
95-th percentile0.03048687252
Maximum0.2148070316
Range0.3586141455
Interquartile range (IQR)0.01724133249

Descriptive statistics

Standard deviation0.02125458754
Coefficient of variation (CV)21.24013396
Kurtosis16.50105948
Mean0.001000680484
Median Absolute Deviation (MAD)0.008582620687
Skewness1.12081547
Sum0.9436416968
Variance0.0004517574914
MonotonicityNot monotonic
2021-09-27T15:03:10.561742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
2.0%
0.0070849892732
 
0.2%
0.024615361142
 
0.2%
0.0016189302852
 
0.2%
-0.0058754629132
 
0.2%
-0.00058174686281
 
0.1%
-0.0016638963271
 
0.1%
0.0038888719351
 
0.1%
-0.0016601427671
 
0.1%
0.0033351565711
 
0.1%
Other values (911)911
96.6%
ValueCountFrequency (%)
-0.14380711391
0.1%
-0.087378681731
0.1%
-0.078260836391
0.1%
-0.077296821871
0.1%
-0.074360167851
0.1%
-0.068220734051
0.1%
-0.06614351431
0.1%
-0.060835150911
0.1%
-0.05991039521
0.1%
-0.059689253441
0.1%
ValueCountFrequency (%)
0.21480703161
0.1%
0.11303339491
0.1%
0.11191762841
0.1%
0.10910005751
0.1%
0.090202161151
0.1%
0.082154833811
0.1%
0.079863066951
0.1%
0.078467797121
0.1%
0.066033318171
0.1%
0.063118237471
0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
no change
764 
rise
93 
drop
86 

Length

Max length9
Median length9
Mean length8.050901379
Min length4

Characters and Unicode

Total characters7592
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno change
2nd rowno change
3rd rowno change
4th rowrise
5th rowno change

Common Values

ValueCountFrequency (%)
no change764
81.0%
rise93
 
9.9%
drop86
 
9.1%

Length

2021-09-27T15:03:10.861905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:03:10.956553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no764
44.8%
change764
44.8%
rise93
 
5.4%
drop86
 
5.0%

Most occurring characters

ValueCountFrequency (%)
n1528
20.1%
e857
11.3%
o850
11.2%
764
10.1%
c764
10.1%
h764
10.1%
a764
10.1%
g764
10.1%
r179
 
2.4%
i93
 
1.2%
Other values (3)265
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6828
89.9%
Space Separator764
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1528
22.4%
e857
12.6%
o850
12.4%
c764
11.2%
h764
11.2%
a764
11.2%
g764
11.2%
r179
 
2.6%
i93
 
1.4%
s93
 
1.4%
Other values (2)172
 
2.5%
Space Separator
ValueCountFrequency (%)
764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6828
89.9%
Common764
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1528
22.4%
e857
12.6%
o850
12.4%
c764
11.2%
h764
11.2%
a764
11.2%
g764
11.2%
r179
 
2.6%
i93
 
1.4%
s93
 
1.4%
Other values (2)172
 
2.5%
Common
ValueCountFrequency (%)
764
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1528
20.1%
e857
11.3%
o850
11.2%
764
10.1%
c764
10.1%
h764
10.1%
a764
10.1%
g764
10.1%
r179
 
2.4%
i93
 
1.2%
Other values (3)265
 
3.5%

Interactions

2021-09-27T15:02:57.007417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:57.186439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:57.368420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:57.736084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:58.142593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:58.495322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:58.712157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:58.916271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:59.076909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:59.238466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:59.480591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:59.669301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:59.838423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:02:59.995771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:00.156428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:00.296254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:00.442682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:00.577584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:00.731938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:00.873623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.015014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.153840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.288401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.425843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.555171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.684779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.821881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:01.963488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:02.103089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:02.247544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:02.393979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:02.531928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:02.670003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:02.821238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:02.973709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:03.122680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:03.276551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:03.433032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:03.581672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:03.732477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:03.888960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:04.047425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:04.204787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:04.355556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:04.509045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:04.654279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:04.801826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:04.956137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:03:05.122262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-27T15:03:11.055118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-27T15:03:11.266831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-27T15:03:11.477425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-27T15:03:11.690427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-27T15:03:11.894397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-27T15:03:05.415185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-27T15:03:05.742062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
02016-08-27 09:30:00Everything is funny about Trump's doctor until you realize he's 90% likely to be our future Surgeon General.levie00000031147577113.1300.006130no change
12016-08-29 16:00:00The Pope's face is one of those, "Shit, it's just a statue of a drone, I thought I was getting a real one" faces. https://t.co/AIURrVdXTtlevie0001108111338113.2900.018391no change
22016-08-30 16:00:006,000 people registered for BoxWorks next week! We'll be making major product announcements. And some Trump jokes. https://t.co/EXn9NZAbk0levie000001534192113.3200.000000no change
32016-09-01 16:00:00Unlike essentially every other kind of challenge a startup runs into, you probably don't hear "it's not rocket science" much at SpaceX.levie00000020355928113.9800.055094rise
42016-09-02 16:00:00Hopefully it's clear by now that promising taco trucks on every corner would, in fact, be the most electable platform to run on.levie0000008186650114.1400.010000no change
52016-09-03 09:30:00BoxWorks is now 5 days away! Grab a free expo pass to join us for the keynotes here: https://t.co/4Pgi6yBzXa https://t.co/4CtLndUeUzlevie0001111864329114.035-0.007426no change
62016-09-04 09:30:00Building an enterprise software company is 95% technology and 5% channeling your inner Taylor Swift. https://t.co/vrXrXt55Rrlevie00011020138503114.070-0.006531no change
72016-09-05 09:30:00"Oh sure, this seems like a totally safe and normal Airbnb to stay in." https://t.co/vfulGB7ftxlevie0001101383479114.105-0.005640no change
82016-09-06 09:30:007,000 people registered for BoxWorks this week! If you can't make it (why!? 😢😟😞😗) we'll be streaming live here: https://t.co/EXn9NZAbk0levie000001638158114.140-0.004751no change
92016-09-07 16:00:00Steps for a great morning: 1. Load https://t.co/OefY681YEc 2. Load https://t.co/MFBvDV5Hnj 3. Experience all your tech wishes come truelevie000002728155114.5200.013967no change

Last rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
9332021-07-06 16:00:00@zlurie Adding $55B in market cap. Heck of a first day on the job!levie0000001134126.340000-0.018994no change
9342021-07-07 09:30:00Could not be more proud or excited for how the Box team is cranking on our product roadmap right now. Lots and lots of epic innovation coming soon for customers this summer and fall!levie000000126236126.5000000.006074no change
9352021-07-07 16:00:00Excited to partner with BT as they continue to leverage the Box Content Cloud! https://t.co/lJQ2PIQSaslevie0000011155125.610001-0.033585drop
9362021-07-09 09:30:00@apoorva_mehta @fidjissimo Congratttttttts!levie000000006125.110001-0.000398no change
9372021-07-11 16:00:00Crazy https://t.co/dRQ7MSW2B4levie000110231051479124.843333-0.015586no change
9382021-07-14 16:00:00If you’re not killing products you’re not working on enough risky ideas. Interesting to see which states are all for free markets, until they’re not.levie000000391881936223.840000-0.018930no change
9392021-07-17 16:00:00@DavidSacks Will text him now @DavidSacks Left here, reporting for duty. For sure came from a lab. @micsolana @rabois Never said mass censorship 😉. Said mass distribution has consequences. I think plenty of innovation starts out great and then we see the follow on effects of it, so it’s worth being critical and introspective of this. Also, FWIW I have no solution to offer, just complaints 😀 @alanknit @rabois Well I guess Keith wins this round, yet again! @peteryared @rabois @raohackr Unless you’ve been going to Lauren Boebert rallies, you might not be the demographic where our issue lies. But also on category #2 that just is a bad public health strategy. @rabois @raohackr Notice I didn’t say Scientists™. I said science 😀. Just talking about vaccines here. @raohackr @rabois Yeah, not going to work in a heavily politicized public health crisis where large groups willfully ignore science. Speech vs. speech isn’t going to solve anything. Anyway, it is what it is 😀. @raohackr @rabois There are 0 things Biden admin could do to convince a meaningful portion of US to get vaccinated when it has become fully politicized and weaponized as an issue. They are a centralized entity fighting a decentralized war 10X their size with the same informational tools. @rabois I don’t think the government should control what’s on social media. Largely, I think social media platforms should decide. But I also think we don’t fully understand how we’re supposed to resolve the consequence of mass distribution of dangerous content. @rabois Alternatively: if people are mad about everyone having a bazooka, just wait until they realize what pistols are used for. And bow and arrows. And cutlery. @rabois Come on though 😀. 17 years into social media we can thoughtfully differentiate small group communication from mass distribution. If we can’t critically think about the consequences of that, we really don’t understand what tech unleashes on the world.levie00000048136381123.303333-0.009773no change
9402021-07-19 09:30:00@rowantrollope @ericsyuan 🥳🎉🥂 congrats you two!levie1000001116123.2600000.005331no change
9412021-07-20 09:30:00Amazing https://t.co/YGDtOVvm92levie00011036145123.1299990.006966no change
9422021-07-20 16:00:00This is a very sad take on entrepreneurship Space innovation from many ventures is just plain win-win-win. More funding of research that will lead to unexpected discoveries, more rocket scientists, more knowledge about the universe, and of course the possibility of tourism and expanding to other planets.levie0000006380970223.7400000.026373rise